class_docstr: Dataset iterator for fitting estimator models, like vocabs, kNN, vectorizers.
  Data is passed as a list of strings(documents).
  Generate batches (for large datasets).
  
  Args:
      data: list of documents
      doc_ids: provided document ids
      seed: random seed for data shuffling
      shuffle: whether to shuffle data during batching
  
  Attributes:
      shuffle: whether to shuffle data during batching
      random: instance of :class:`Random` initialized with a seed
      data: list of documents
      doc_ids: provided by a user ids or generated automatically ids
docstr: Get doc content by id.
  
  Args:
      doc_id: an id for a doc which content should be extracted
  
  Returns:
      doc content as a string if id exists or raise an error
docstr_tok: ["Get","doc","content","by","id",".","Args",":","doc_id",":","an","id","for","a","doc","which","content","should","be","extracted","Returns",":","doc","content","as","a","string","if","id","exists","or","raise","an","error"]
code: def get_doc_content(self, doc_id: Any) -> Optional[str]:
      """Get doc content by id.
  
      Args:
          doc_id: an id for a doc which content should be extracted
  
      Returns:
          doc content as a string if id exists or raise an error
  
      """
      return self.data[doc_id]
  
file_path: DeepPavlov/deeppavlov/core/data/data_fitting_iterator.py
imports: from logging import getLogger
  from random import Random
  from typing import List, Generator, Tuple, Any, Optional
  from deeppavlov.core.common.registry import register
label_desc: Use image node_id 3 for calling the DataFittingIterator obj's underlying member method code with example usage: obj.get_doc_content(doc_id) and returns: self